Poster Paper Data Integration for Supporting Biomedical Knowledge Graph Creation at Large-Scale

2018 ◽  
pp. 91-96
Author(s):  
Samaneh Jozashoori ◽  
Tatiana Novikova ◽  
Maria-Esther Vidal
Keyword(s):  
Data Integration ◽  
Large Scale ◽  
Knowledge Graph ◽  
Biomedical Knowledge ◽  
Poster Paper

scholarly journals Disease ontologies for knowledge graphs

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Natalja Kurbatova ◽  
Rowan Swiers
Keyword(s):  
Data Integration ◽  
Data Sources ◽  
Knowledge Graph ◽  
Disease Area ◽  
Biomedical Knowledge ◽  
Disease Ontology ◽  
Disease Clusters ◽  
Common Task ◽  
Knowledge Graphs ◽  
Elegant Solution

Abstract Background Data integration to build a biomedical knowledge graph is a challenging task. There are multiple disease ontologies used in data sources and publications, each having its hierarchy. A common task is to map between ontologies, find disease clusters and finally build a representation of the chosen disease area. There is a shortage of published resources and tools to facilitate interactive, efficient and flexible cross-referencing and analysis of multiple disease ontologies commonly found in data sources and research. Results Our results are represented as a knowledge graph solution that uses disease ontology cross-references and facilitates switching between ontology hierarchies for data integration and other tasks. Conclusions Grakn core with pre-installed “Disease ontologies for knowledge graphs” facilitates the biomedical knowledge graph build and provides an elegant solution for the multiple disease ontologies problem.

Large-scale Data Integration for Facilities Analytics: Challenges and Opportunities

2020 ◽  
Author(s):  
Balaje T. Thumati ◽  
Halasya Siva Subramania ◽  
Rajeev Shastri ◽  
Karthik Kalyana Kumar ◽  
Nicole Hessner ◽  
...  
Keyword(s):  
Data Integration ◽  
Large Scale ◽  
Large Scale Data ◽  
Challenges And Opportunities ◽  
Scale Data

scholarly journals ASER: A Large-scale Eventuality Knowledge Graph

2020 ◽  
Author(s):  
Hongming Zhang ◽  
Xin Liu ◽  
Haojie Pan ◽  
Yangqiu Song ◽  
Cane Wing-Ki Leung
Keyword(s):  
Large Scale ◽  
Knowledge Graph

Knowledge Graph Completion to Solve University Campus Issues

2020 ◽  
Vol 1 (3) ◽  
pp. 333-350
Author(s):  
Yuto Tsukagoshi ◽  
Takahiro Kawamura ◽  
Yuichi Sei ◽  
Yasuyuki Tahara ◽  
Akihiko Ohsuga
Keyword(s):  
Data Integration ◽  
Conventional Method ◽  
Current Literature ◽  
Statistical Data ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Knowledge Graph ◽  
University Campus ◽  
Combining Data ◽  
Conventional Methods

A number of urban challenges are encountered by modern societies. Governments, businesses and public bodies need to make statistical data widely available in order to tackle these challenges. Nonetheless, current literature and data are problematic; they have inaccuracies which lead to less effective methods of resolving these issues. This research aims to solve this challenge by thinking of a university campus as a microcosm of society, implementing a data integration schema, and combining data into a knowledge graph. Existing completion methods will then be applied and updated. Especially in regards to bicycle environment, our knowledge graph was tailored and evaluated in line with conventional methods, and secondly with our proposed derivative methods. Roughly 650 pieces of parking data, with various dates and times, was contrasted with each time's mean absolute error. Our approach accurately projected 54.5 more bicycles than the conventional method.

scholarly journals Turning Informal Thesauri into Formal Ontologies: A Feasibility Study on Biomedical Knowledge Re-Use

2003 ◽  
Vol 4 (1) ◽  
pp. 94-97 ◽  
Author(s):  
Udo Hahn
Keyword(s):  
Domain Knowledge ◽  
Large Scale ◽  
Description Logics ◽  
Biomedical Domain ◽  
Biomedical Knowledge ◽  
Knowledge Conversion ◽  
Formal Knowledge ◽  
Knowledge Repositories ◽  
Formal Ontologies ◽  
Rigorous Description

This paper reports a large-scale knowledge conversion and curation experiment. Biomedical domain knowledge from a semantically weak and shallow terminological resource, the UMLS, is transformed into a rigorous description logics format. This way, the broad coverage of the UMLS is combined with inference mechanisms for consistency and cycle checking. They are the key to proper cleansing of the knowledge directly imported from the UMLS, as well as subsequent updating, maintenance and refinement of large knowledge repositories. The emerging biomedical knowledge base currently comprises more than 240 000 conceptual entities and hence constitutes one of the largest formal knowledge repositories ever built.

Biomedical Knowledge Graph Embeddings for Personalized Medicine

2021 ◽  
pp. 584-595
Author(s):  
Joana Vilela ◽  
Muhammad Asif ◽  
Ana Rita Marques ◽  
João Xavier Santos ◽  
Célia Rasga ◽  
...  
Keyword(s):  
Personalized Medicine ◽  
Knowledge Graph ◽  
Graph Embeddings ◽  
Biomedical Knowledge

scholarly journals OpenBioLink: a benchmarking framework for large-scale biomedical link prediction

2020 ◽  
Vol 36 (13) ◽  
pp. 4097-4098 ◽  
Author(s):  
Anna Breit ◽  
Simon Ott ◽  
Asan Agibetov ◽  
Matthias Samwald
Keyword(s):  
Link Prediction ◽  
Large Scale ◽  
Source Code ◽  
Machine Learning Algorithms ◽  
Knowledge Networks ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Biomedical Knowledge ◽  
High Quality ◽  
Baseline Evaluation

Abstract Summary Recently, novel machine-learning algorithms have shown potential for predicting undiscovered links in biomedical knowledge networks. However, dedicated benchmarks for measuring algorithmic progress have not yet emerged. With OpenBioLink, we introduce a large-scale, high-quality and highly challenging biomedical link prediction benchmark to transparently and reproducibly evaluate such algorithms. Furthermore, we present preliminary baseline evaluation results. Availability and implementation Source code and data are openly available at https://github.com/OpenBioLink/OpenBioLink. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals A Medical Informatics Perspective on Health Informatics 3.0

2011 ◽  
Vol 20 (01) ◽  
pp. 30-32
Author(s):  
P. Ruch ◽  
Keyword(s):  
Semantic Web ◽  
Health Informatics ◽  
Large Scale ◽  
Biomedical Data ◽  
Biomedical Knowledge ◽  
Data Set ◽  
Web 3.0 ◽  
Research Fields ◽  
Emerging Trends ◽  
Excellent Research

SummaryTo summarize current advances of the so-called Web 3.0 and emerging trends of the semantic web.We provide a synopsis of the articles selected for the IMIA Yearbook 2011, from which we attempt to derive a synthetic overview of the today’s and future activities in the field.while the state of the research in the field is illustrated by a set of fairly heterogeneous studies, it is possible to identify significant clusters. While the most salient challenge and obsessional target of the semantic web remains its ambition to simply interconnect all available information, it is interesting to observe the developments of complementary research fields such as information sciences and text analytics. The combined expression power and virtually unlimited data aggregation skills of Web 3.0 technologies make it a disruptive instrument to discover new biomedical knowledge. In parallel, such an unprecedented situation creates new threats for patients participating in large-scale genetic studies as Wjst demonstrate how various data set can be coupled to re-identify anonymous genetic information.The best paper selection of articles on decision support shows examples of excellent research on methods concerning original development of core semantic web techniques as well as transdisciplinary achievements as exemplified with literature-based analytics. This selected set of scientific investigations also demonstrates the needs for computerized applications to transform the biomedical data overflow into more operational clinical knowledge with potential threats for confidentiality directly associated with such advances. Altogether these papers support the idea that more elaborated computer tools, likely to combine heterogeneous text and data contents should soon emerge for the benefit of both experimentalists and hopefully clinicians.

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